The development and psychometric properties of the grudge aspect measure
Bibliographic record
Abstract
Abstract Grudges are a common response to an interpersonal transgression that have received limited empirical attention. In the current research, we developed a self‐report measure of holding a grudge—the grudge aspect measure. The items were based on key findings from van Monsjou et al.'s (2021) thematic analysis: the six underlying components of holding a grudge identified in their analysis (need for validation, moral superiority, inability to let go, latency, sever ties, and expectations of the future); the cyclical process of holding a grudge which is characterized by persistent negative affect and intrusive thoughts that interfere with one's quality of life; and the definition of a grudge as sustained feelings of hurt and anger that dissipate over time but are easily reignited. Across three studies, we validated an 18‐item scale capturing three aspects of holding a grudge: disdain , feelings of dislike and intolerance for the transgressor; emotional persistence , sustained negative affect such as anger and hurt; and perceived longevity , perceptions of never being able to let go of the grudge. As expected, these aspects of holding a grudge were linked to less forgiveness and greater general unforgiveness, as well as revenge, avoidance, and rumination. Topics for future research are discussed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".